Lead MLOps Engineer

Responsibilities:

• Drive R&D and engineering for AI Infrastructure optimization within CloudKeeper's FinOps for AI platform building the Tuner AI / Commit AI capability on GPU and ML workloads

• Design and build optimization engines for GPU right-sizing, idle shutdown, spot migration with checkpoint/resume automation, inference batching, quantization, and model placement

• Extend the optimization stack to LLM-era workloads — caching, model routing, dynamic batching, prompt optimization, RAG-aware architectures

• Partner with the Lens AI team to translate GPU and ML workload signals into actionable, dollar-quantified optimization recommendations for customers

• Work cross-functionally with product, platform, and customer success teams to ship optimization features end-to-end (data ingestion optimization engine customer-facing recommendation)

• Lead technical direction for AI workload optimization, set engineering standards, and mentor the ML / MLOps engineering bench as the AI Infrastructure pillar scales

• (Lead level) Hire, ramp, and grow a team of ML infrastructure engineers as headcount expands

Must Have:

• B.E / B.Tech / M.Tech / MCA with 7+ years of hands-on engineering experience

• Production experience with GPU workloads — has measurably optimized GPU utilization, throughput, or cost in a real production environment, not just academic / lab work

• Strong performance engineering background — must come ready with a concrete optimization story including before/after metrics (latency, throughput, or cost reduction)

• Strong Python + Linux + systems fundamentals

• Solid understanding of the ML model lifecycle — training, serving, inference — able to reason about what is running on the GPU and why

• MLOps fluency — model deployment, monitoring, observability, GPU cluster operations

• Hands-on with cloud GPU instances (AWS P5 / G6, Azure ND series, GCP A3, or equivalent) and Kubernetes-based GPU orchestration (EKS / AKS / GKE GPU node pools, Karpenter, Run:ai, NVIDIA GPU Operator, or similar)

• Familiarity with at least one modern LLM inference framework — vLLM, TGI, Triton, SGLang, Ray Serve, or BentoML

• Strong communication skills — able to translate deep technical optimization into customer / business outcomes

• (Lead level) Experience managing or technically leading a team of 3+ engineers

Good to Have:

• Deep LLM-era optimization expertise — KV caching, semantic caching, model routing, dynamic batching, quantization (FP16 INT8 INT4), model distillation, structured outputs

• Familiarity with LLM workload patterns — RAG, agents, embeddings, vector databases (Pinecone, Weaviate, Qdrant)

• CUDA, NCCL, mixed-precision training and inference

• Experience with managed ML training platforms — SageMaker, Azure ML, Vertex AI, Databricks Mosaic

• Exposure to GPU-native clouds — CoreWeave, Lambda Labs, RunPod, Crusoe

• Open source contributions to ML infrastructure projects — vLLM, llama.cpp, TGI, Ray, Triton, KubeRay

• Adjacent experience in cloud cost optimization / FinOps — Spot.io, ScaleOps, Granulate, CAST AI

• Comfort with Agile methodology and modern engineering practices (CI/CD, code review, observability)